def _affine_backward(self, x, w, b, dout):
layer = Linear(w.shape[0], w.shape[1])
layer.weight = w
layer.bias = b
tmp = layer.forward(x)
layer.backward(dout)
return layer.dx, layer.dw, layer.db
class NN2(object):
def __init__(self, in_layer_size, hidden_layer_size, out_layer_size):
self.fc1 = Linear(in_layer_size, hidden_layer_size)
self.ac1 = ReLu()
self.fc2 = Linear(hidden_layer_size, out_layer_size)
def forward(self, x):
s1 = self.fc1.forward(x)
a1 = self.ac1.forward(s1)
a2 = self.fc2.forward(a1)
return a2
def update(self, params):
self.fc1.update([params[0]])
self.fc2.update([params[1]])
def backward(self, dL_dy2):
'''
output dy/dw2 = d(f(wx+b))/dw = x
output dy/dw1 = d(f(wx+b))/dw = x
'''
#dL_ds2 = self.ac2.backward(dL_dy2)
dL_dy1 = self.fc2.backward(dL_dy2)
dL_ds1 = self.ac1.backward(dL_dy1)
dL_dy0 = self.fc1.backward(dL_ds1)
return dL_dy0
def param(self):
return [self.fc1.param()[0], self.fc2.param()[0]]
class NN(object):
def __init__(self, in_layer_size, out_layer_size):
self.fc1 = Linear(in_layer_size, out_layer_size, bias=False)
self.ac1 = Tanh()
def forward(self, x):
s1 = self.fc1.forward(x)
a1 = self.ac1.forward(s1)
return a1
def update(self, params):
#print("W:", params[0].shape)
self.fc1.update([params[0]])
if len(params) > 1:
#print("R:", len([params[1]]))
self.ac1.update(params[1])
#print("W:",self.fc1.param()[0][0])
#print("dW:",self.fc1.param()[0][1])
def backward(self, dL_dy):
'''
output dy/dw2 = d(f(wx+b))/dw = x
output dy/dw1 = d(f(wx+b))/dw = x
'''
#print(dL_dy)
dL_ds = self.ac1.backward(dL_dy)
dL_dy0 = self.fc1.backward(dL_ds)
#print(dL_dy0)
return dL_dy0
def param(self):
return [self.fc1.param()[0], self.ac1.param()[0]]
bias = torch.Tensor([1, 2, 3, 4])
bias.shape
'''
input = input[:, :, None]
weights.matmul(input).squeeze() + bias'''
lin = Linear(5, 4, ReLU())
output = lin.forward(input)
target = torch.Tensor([[0, 0, 1, 0],
[0, 0, 0, 1],
[0, 0, 1, 0]])
d_loss = dloss(output, target)
prev_dl_dx = lin.backward(d_loss)
prev_dl_dx.shape
ex_dloss = torch.Tensor([[.1, .2, .2, .1],
[.1, .2, .2, .1],
[.1, .2, .2, .1]])
dl_ds = drelu(output)*ex_dloss
dl_db = dl_ds.sum()/dl_ds.shape[0]
(drelu(output)*ex_dloss).sum(0)
prev dl_dx = weights.transpose(1,2).matmul(dl_ds[:, :, None]).squeeze()
